Information Systems combine process and data into a single operating unit to support a wide variety of functions including, for example, transaction process systems, management information systems, decision support systems, executive information systems, data warehouses, enterprise resource planning, enterprise systems, expert systems, geographic information systems, global information systems and office automation. Organizations implement automated information systems are for the purpose of improving the effectiveness and efficiency of that organization.
Information Systems may be organized into “landscapes” of component systems that help manage the flow of function and data change. A common landscape includes:                Production—essential to the daily operations support of an organization        Support—operations support, source of emergency production corrections        Testing—change validation system        Training—end-user training        Development—change development system        Sandbox—experimental change system        
FIG. 1 is a block diagram that shows how the landscape may be organized to support the flow of function and data change. In the example shown, information system 100 includes a production (or “live”) system 102 used to provide daily data processing and related services, for example to an enterprise or other organization and/or its downstream consumers or other users. A support organization and associated infrastructure 104 provides support to the daily operations performed using the production system 102. A test component 106 may be used by the support organization, for example to troubleshoot and/or test fixes applied to production system 102. Test component 106 may also be used to support a training organization and/or infrastructure 108, which may be used to train users of production system 102, for example. A development component 110 may be used to develop and ready for deployment to production system 102, after testing and training for example using test component 106 and training infrastructure 108, changes to the application code or other code deployed on production system 102. Development component 110 may be used in the example shown to develop for deployment experimental changes first investigated using a sandbox component 112, which enables code to be written and executed in a secure environment well isolated from production system 102.
Data quality may vary considerably across a typical landscape's component systems. The best data may be termed “production-grade” as it is contained in the production component system, such as production system 102 of FIG. 1. This system is used to support everyday operations of the organization. Its functions are most frequently used and in the most varied of usage scenarios. Therefore, its data is of the highest quality, reflecting the full and varied uses cases that the Information System has been implemented by the organization to support.
The three key reasons for poorer data quality in the other component systems are: (i) production data is larger now than at any point in history: copying terabytes of data between systems is too slow, too disruptive and too expensive; (ii) production data invariably contains confidential data that should not be copied unless securely masked; (iii) information needed to ensure the full integrity of data copied between component systems is hidden in the application logic that makes up the function elements of the Information System.
But without access to production-grade data, testing, training, development and even experimental component systems suffer a reduction in quality. Information Systems combine function and data into a single operating unit. Functions that operate on poor-quality data may give poor-quality results.
In short, there is significant quality advantage to be gained from access to production-grade data across the Information System landscape, but significant challenges (size, time, cost, security and integrity) work against achieving this goal.